Correlation Between SERI INDUSTRIAL and PURE FOODS
Can any of the company-specific risk be diversified away by investing in both SERI INDUSTRIAL and PURE FOODS at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining SERI INDUSTRIAL and PURE FOODS into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between SERI INDUSTRIAL EO and PURE FOODS TASMANIA, you can compare the effects of market volatilities on SERI INDUSTRIAL and PURE FOODS and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in SERI INDUSTRIAL with a short position of PURE FOODS. Check out your portfolio center. Please also check ongoing floating volatility patterns of SERI INDUSTRIAL and PURE FOODS.
Diversification Opportunities for SERI INDUSTRIAL and PURE FOODS
-0.42 | Correlation Coefficient |
Very good diversification
The 3 months correlation between SERI and PURE is -0.42. Overlapping area represents the amount of risk that can be diversified away by holding SERI INDUSTRIAL EO and PURE FOODS TASMANIA in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on PURE FOODS TASMANIA and SERI INDUSTRIAL is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on SERI INDUSTRIAL EO are associated (or correlated) with PURE FOODS. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of PURE FOODS TASMANIA has no effect on the direction of SERI INDUSTRIAL i.e., SERI INDUSTRIAL and PURE FOODS go up and down completely randomly.
Pair Corralation between SERI INDUSTRIAL and PURE FOODS
Assuming the 90 days trading horizon SERI INDUSTRIAL EO is expected to under-perform the PURE FOODS. In addition to that, SERI INDUSTRIAL is 1.47 times more volatile than PURE FOODS TASMANIA. It trades about -0.04 of its total potential returns per unit of risk. PURE FOODS TASMANIA is currently generating about 0.13 per unit of volatility. If you would invest 0.85 in PURE FOODS TASMANIA on October 6, 2024 and sell it today you would earn a total of 0.20 from holding PURE FOODS TASMANIA or generate 23.53% return on investment over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Against |
Strength | Very Weak |
Accuracy | 98.33% |
Values | Daily Returns |
SERI INDUSTRIAL EO vs. PURE FOODS TASMANIA
Performance |
Timeline |
SERI INDUSTRIAL EO |
PURE FOODS TASMANIA |
SERI INDUSTRIAL and PURE FOODS Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with SERI INDUSTRIAL and PURE FOODS
The main advantage of trading using opposite SERI INDUSTRIAL and PURE FOODS positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if SERI INDUSTRIAL position performs unexpectedly, PURE FOODS can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in PURE FOODS will offset losses from the drop in PURE FOODS's long position.SERI INDUSTRIAL vs. Columbia Sportswear | SERI INDUSTRIAL vs. Singapore Telecommunications Limited | SERI INDUSTRIAL vs. Hutchison Telecommunications Hong | SERI INDUSTRIAL vs. COLUMBIA SPORTSWEAR |
PURE FOODS vs. Danone SA | PURE FOODS vs. Superior Plus Corp | PURE FOODS vs. NMI Holdings | PURE FOODS vs. Origin Agritech |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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